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Novel wolf pack optimization algorithm for intelligent medical treatment personalized recommendation

The Journal of China Universities of Posts and Telecommunications 2018年第6期25卷 44-57页

作者： Wang Dongxing Qian Xu Liu Kang Guan Xinyu School of Mechanical Electronic and Information Engineering China University of Mining and Technology Department of Mechanical Engineering Tsinghua University

To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy(ORRSS-WPOA) for an adaptive shrinking region. Firstly, via the oppositional raiding method(ORM), each wolf has bigger probability of approaching the leader wolf, which makes the exploration of the wolf pack enhanced as a whole. In another word, the wolf pack is not easy to fall into local optimum. Moreover, random searching strategy(RSS) for an adaptive shrinking region is adopted to strengthen exploitation, which enables any wolf to be more likely to find the optimum in some a given region, so macroscopically the wolf pack is easier to find the global optimal in the given range. Finally, a fitness function was designed to judge the appropriateness between a certain patient and a hospital. The performance of the ORRSS-WPOA was comprehensively evaluated by comparing it with several other competitive algorithms on ten classical benchmark functions and the simulated fitness function aimed to solve the problem mentioned above. Under the same condition, our experimental results indicated the excellent performance of ORRSS-WPOA in terms of solution quality and computational efficiency.

关键词： wolf pack optimization opposition raiding random search adaptive shrinking

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